Hu, J., H. Zhang, S. -H. Chen, Environmental Science & Technology, 48, 4971-4979, doi:10.1021/es404809j., , Q. Ying, and M. J. Kleeman, 2014: Predicting primary PM2.5 and PM0.1 trace composition for epidemiological studies in California.
|Title:||Predicting primary PM2.5 and PM0.1 trace composition for epidemiological studies in California|
|Abstract:||The University of California-Davis_Primary (UCD_P) chemical transport model was developed and applied to compute the primary airborne particulate matter (PM) trace chemical concentrations from 900 sources in California through a simulation of atmospheric emissions, transport, dry deposition and wet deposition for a 7-year period (2000–2006) with results saved at daily time resolution. A comprehensive comparison between monthly average model results and available measurements yielded Pearson correlation coefficients (R) ≥0.8 at ≥5 sites (out of a total of eight) for elemental carbon (EC) and nine trace elements: potassium, chromium, zinc, iron, titanium, arsenic, calcium, manganese, and strontium in the PM₂.₅ size fraction. Longer averaging time increased the overall R for PM₂.₅ EC from 0.89 (1 day) to 0.94 (1 month), and increased the number of species with strong correlations at individual sites. Predicted PM₀.₁ mass and PM₀.₁ EC exhibited excellent agreement with measurements (R = 0.92 and 0.94, respectively). The additional temporal and spatial information in the UCD_P model predictions produced population exposure estimates for PM₂.₅ and PM0.1that differed from traditional exposure estimates based on information at monitoring locations in California Metropolitan Statistical Areas, with a maximum divergence of 58% at Bakersfield. The UCD_P model has the potential to improve exposure estimates in epidemiology studies of PM trace chemical components and health.|
|Copyright Information:||Copyright 2014 American Chemical Society.|
|OpenSky citable URL:||ark:/85065/d7xs5wbk|